103 research outputs found
Norm Monitoring under Partial Action Observability
In the context of using norms for controlling multi-agent systems, a vitally
important question that has not yet been addressed in the literature is the
development of mechanisms for monitoring norm compliance under partial action
observability. This paper proposes the reconstruction of unobserved actions to
tackle this problem. In particular, we formalise the problem of reconstructing
unobserved actions, and propose an information model and algorithms for
monitoring norms under partial action observability using two different
processes for reconstructing unobserved actions. Our evaluation shows that
reconstructing unobserved actions increases significantly the number of norm
violations and fulfilments detected.Comment: Accepted at the IEEE Transaction on Cybernetic
Resolving Multi-party Privacy Conflicts in Social Media
Items shared through Social Media may affect more than one user's privacy ---
e.g., photos that depict multiple users, comments that mention multiple users,
events in which multiple users are invited, etc. The lack of multi-party
privacy management support in current mainstream Social Media infrastructures
makes users unable to appropriately control to whom these items are actually
shared or not. Computational mechanisms that are able to merge the privacy
preferences of multiple users into a single policy for an item can help solve
this problem. However, merging multiple users' privacy preferences is not an
easy task, because privacy preferences may conflict, so methods to resolve
conflicts are needed. Moreover, these methods need to consider how users' would
actually reach an agreement about a solution to the conflict in order to
propose solutions that can be acceptable by all of the users affected by the
item to be shared. Current approaches are either too demanding or only consider
fixed ways of aggregating privacy preferences. In this paper, we propose the
first computational mechanism to resolve conflicts for multi-party privacy
management in Social Media that is able to adapt to different situations by
modelling the concessions that users make to reach a solution to the conflicts.
We also present results of a user study in which our proposed mechanism
outperformed other existing approaches in terms of how many times each approach
matched users' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE
Transactions on Knowledge and Data Engineering, IEEE Transactions on
Knowledge and Data Engineering, 201
Social computing privacy and online relationships
Social computing has revolutionized interpersonal communication. It has introduced the aspect of social relationships which people can utilize to communicate with the vast spectrum of their contacts. However, the major Online Social Networks (OSNs) have been found to be falling short of appropriately accommodating these relationships in their privacy controls which leads to undesirable consequences for the users. This paper highlights some of the shortcomings of the OSNs with respect to their handling of social relationships and enumerates numerous challenges which need to be conquered in order to provide users with a truly social experienc
Towards implicit contextual integrity
Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing users from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that expresses the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users’ relationships are constantly evolving; and the norms for information sharing are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks. This position paper highlights the limitations of current research to tackle the problem of exchanging inappropriate information and undesired dissemination of information and outlines the desiderata for a new vision that we call Implicit Contextual Integrity
IMPROVE:Identifying Minimal PROfile VEctors for similarity based access control
There is ample evidence which shows that social media users struggle to make appropriate access control decisions while disclosing their information and smarter mechanisms are needed to assist them. Using profile information to ascertain similarity between users and provide suggestions to them during the process of making access control decisions has been put forth as a possible solution to this problem. This paper presents an empirical study aimed at identifying the minimal subset of attributes which are most suitable for being used to create profile vectors for the purpose of predicting access control decisions. We begin with an exhaustive list of 30 profile attributes and identify a subset of 2 profile attributes which are shown to be sufficient in obtaining similarity between profiles and predicting access control decisions with the same accuracy as previous models. We demonstrate that using this pair of attributes will help mitigate the challenges encountered by similarity based access control mechanisms
Assured deletion in the cloud:requirements, challenges and future directions
Inadvertent exposure of sensitive data is a major concern for potential cloud customers. Much focus has been on other data leakage vectors, such as side channel attacks, while issues of data disposal and assured deletion have not received enough attention to date. However, data that is not properly destroyed may lead to unintended disclosures, in turn, resulting in heavy financial penalties and reputational damage. In non-cloud contexts, issues of incomplete deletion are well understood. To the best of our knowledge, to date, there has been no systematic analysis of assured deletion challenges in public clouds. In this paper, we aim to address this gap by analysing assured deletion requirements for the cloud, identifying cloud features that pose a threat to assured deletion, and describing various assured deletion challenges. Based on this discussion, we identify future challenges for research in this area and propose an initial assured deletion architecture for cloud settings. Altogether, our work offers a systematization of requirements and challenges of assured deletion in the cloud, and a well-founded reference point for future research in developing new solutions to assured deletion
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